EP 4033419 A1 20220727 - OUTLIER DETECTION FOR SPECTROSCOPIC CLASSIFICATION
Title (en)
OUTLIER DETECTION FOR SPECTROSCOPIC CLASSIFICATION
Title (de)
AUSREISSERERKENNUNG FÜR SPEKTROSKOPISCHE KLASSIFIZIERUNG
Title (fr)
DÉTECTION DE VALEURS ABERRANTES POUR CLASSIFICATION SPECTROSCOPIQUE
Publication
Application
Priority
US 202117248333 A 20210120
Abstract (en)
In some implementations, a device may determine that an unknown sample is an outlier sample by using an aggregated classification model. The device may determine that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample. The device may cause one or more actions based on determining the one or more spectroscopic measurements are not performed accurately.
IPC 8 full level
G06N 20/10 (2019.01)
CPC (source: CN EP)
G06F 18/214 (2023.01 - CN); G06F 18/24 (2023.01 - CN); G06N 20/10 (2018.12 - EP)
Citation (search report)
- [XI] US 2019236333 A1 20190801 - HSIUNG CHANGMENG [US], et al
- [A] MANIRUZZAMAN MD ET AL: "Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers", JOURNAL OF MEDICAL SYSTEMS, SPRINGER US, NEW YORK, vol. 42, no. 5, 10 April 2018 (2018-04-10), pages 1 - 17, XP036507803, ISSN: 0148-5598, [retrieved on 20180410], DOI: 10.1007/S10916-018-0940-7
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
DOCDB simple family (publication)
DOCDB simple family (application)
EP 21204170 A 20211022; CN 202111253583 A 20211027